Computer Science > Machine Learning
[Submitted on 14 Jun 2021 (v1), last revised 1 Nov 2021 (this version, v2)]
Title:Learning to Combine Per-Example Solutions for Neural Program Synthesis
View PDFAbstract:The goal of program synthesis from examples is to find a computer program that is consistent with a given set of input-output examples. Most learning-based approaches try to find a program that satisfies all examples at once. Our work, by contrast, considers an approach that breaks the problem into two stages: (a) find programs that satisfy only one example, and (b) leverage these per-example solutions to yield a program that satisfies all examples. We introduce the Cross Aggregator neural network module based on a multi-head attention mechanism that learns to combine the cues present in these per-example solutions to synthesize a global solution. Evaluation across programs of different lengths and under two different experimental settings reveal that when given the same time budget, our technique significantly improves the success rate over PCCoder [Zohar et. al 2018] and other ablation baselines. The code, data and trained models for our work can be found at this https URL.
Submission history
From: Disha Shrivastava [view email][v1] Mon, 14 Jun 2021 05:48:12 UTC (3,330 KB)
[v2] Mon, 1 Nov 2021 14:38:49 UTC (1,645 KB)
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